A Lightweight Network for Radar Specific Emitter Identification via Differential Constellation Figure

Radar specific emitter identification aims to recognize individual radar emitters based on subtle differences in transmitted signals. This paper proposes using differential constellation figure (DCF) features extracted from received radar signals for this task. A compact deep neural network called AL-ResNet classifies the DCF features to identify individual radar emitters. Experiments on simulated radar signals show the proposed DCF method achieving 91.76 % accuracy in recognizing 6 radar emitters using ResNet50 architecture, outperforming techniques using other signal features. Nevertheless, experiments on the lightweight model ALResNet shows efficient deployment of the utilized network.